Artificial intelligence-based load forecasting and scheduling for virtual power plants

With the advancement of global energy transition, virtual power plant (VPP), as a new type of distributed energy management system, plays an important role in enhancing the flexibility, stability and sustainability of the power system. Load forecasting and scheduling are the core tasks in the operation of virtual power plants, which directly affect the economy and reliability of the system. Traditional load forecasting methods and scheduling strategies show large limitations when facing complex power demand fluctuations, intermittency and uncertainty of renewable energy sources. The introduction of Artificial Intelligence (AI)-based technologies, especially the application of machine learning, deep learning and other methods, provides new solutions for load forecasting and scheduling in virtual power plants. This paper discusses the application of AI in virtual power plant load forecasting and scheduling, analyzes existing forecasting methods and scheduling strategies, and focuses on the advantages of intelligent algorithms in improving forecasting accuracy and optimizing scheduling decisions. Through case studies, this paper demonstrates the great potential of AI technology in improving the operational efficiency of virtual power plants and reducing costs. Combined with the development of current technology, this paper looks forward to the prospects and challenges of AI application in the future development of virtual power plants.